Adaptation is central to biological motor control because our muscles, our motor plant, and the environment that we interact with have time varying properties. Some of these properties change suddenly and temporarily while others change slowly and may last a long time. We suggest that the way that the brain learns motor control is to a great extent a reflection of these time scales of change. In effect, we propose that the brain treats adaptation as a statistical problem that includes prior information about timescales of change. Our new approach solves two major problems which have significantly curtailed our current understanding of the neural basis of adaptation: (1) Current models of movement adaptation focus almost exclusively on error. On every trial, the error is associated with the present context and the system adapts its internal model by changing parameter values or weights. However, there is ample evidence that behavior can change not only as a function of the magnitude of errors, but also as a function of when these errors are made. Our approach predicts the strong dependence on the temporal and contextual history of the training trials in adaptation experiments. (2) Traditional motor adaptation theories assume that the CNS chooses a desired trajectory, estimates internal model parameters through trial and error, and then produces motor commands that move the limb along this desired trajectory. However, movements have costs and gains that are often described in terms of their end result, not a specific trajectory. Indeed, close inspection of behavior during adaptation reveals clear deviations from the predictions of a desired trajectory assumption. Here we treat the problems of uncertainty about the world and our motor plant as well as cost functions in a single framework. We suggest a new theory to guide experiments in sensorimotor learning. What we are proposing here is a fundamental shift away from the current focus on motor error and desired trajectories - ideas that have been the mainstays of sensorimotor research. First, we link learning of internal models to a causal structure of how the body might be affected by real world perturbations. Next, we link changes in internal models to changes in sensorimotor control. As a result, we suggest that the adaptive behavior that we and others have measured is really a result of two concurrent computational processes: statistical formulation of internal models (a process akin to system identification), and the use of those internal models in the framework of optimal control to produce motor commands. Development of this new theoretical framework will ultimately shed light on the computations that are performed by structures in the brain that participate in control of our movements.